【摘要】 网络化测试系统通常由智能仪器组成,采用传统的集中式数据处理结构一方面造成仪器内部计算资源的浪费,同时也对网络带宽带来较大的压力。为解决上述问题,提出了一种利用智能仪器内部计算资源进行并行数据处理的计算架构。提出了一种双层计算资源模型,在顶层使用PBS(portable batch system)作业管理系统,实现在网络化测试系统中的物理资源管理和计算节点分配;在底层使用隔离技术,基于Linux的Cgroups内核特性构建资源容器,实现节点内部的计算资源有效隔离。并行任务基于MPI非虚拟化并行计算平台实现,利用并行FFT算法对该计算模式进行了验证。实验结果表明,该计算架构具有良好的可行性和实用性。作为并行计算在网络化自动测试领域的拓展,具有很好的研究前景及实用价值。更多还原

【Abstract】 Networked test system is generally composed of intelligent instruments. There is an allpervading problem in the traditional centralized data processing method that the structure results in potential wasting of computing resources on the intelligent instruments. Besides,data processing in centralized architecture brings greater pressure to the network bandwidth. In order to solve the problems above,a computing mode of parallel data processing is proposed,which can make full use of the internal calculation resources in the devices. In particular,a double-layer computing resource model is proposed. At the top level,a job management system called portable batch system( PBS) for cluster is used to manage the physical resources and carry calculation node allocation in net-centric automatic test system. In addition,Linux containers are built to achieve internal computing resource isolation effectively within nodes,where isolation technology is used based on control groups( C-groups) kernel features at the bottom. Parallel tasks are conducted based on message passing interface( MPI),which is a non-virtualized parallel computing platform. This is verified using parallel FFT algorithm. The experimental results show that the parallel data processing architecture is of high feasibility and utility. As an expansion on parallel computing in the field of net-centric automation test,this article has application prospect as well as practicability.更多还原